Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization
This incremental improvement addresses computational bottlenecks in multi-task learning for applications like biomedical informatics and computer vision.
The paper tackles the challenge of solving the non-smooth l2,1-norm regularized regression for multi-task feature selection by reformulating it into smooth convex optimization problems solvable via Nesterov's method, achieving efficient computation with linear-time projections in empirical evaluations.
The problem of joint feature selection across a group of related tasks has applications in many areas including biomedical informatics and computer vision. We consider the l2,1-norm regularized regression model for joint feature selection from multiple tasks, which can be derived in the probabilistic framework by assuming a suitable prior from the exponential family. One appealing feature of the l2,1-norm regularization is that it encourages multiple predictors to share similar sparsity patterns. However, the resulting optimization problem is challenging to solve due to the non-smoothness of the l2,1-norm regularization. In this paper, we propose to accelerate the computation by reformulating it as two equivalent smooth convex optimization problems which are then solved via the Nesterov's method-an optimal first-order black-box method for smooth convex optimization. A key building block in solving the reformulations is the Euclidean projection. We show that the Euclidean projection for the first reformulation can be analytically computed, while the Euclidean projection for the second one can be computed in linear time. Empirical evaluations on several data sets verify the efficiency of the proposed algorithms.